text: import numpy as np
  from scipy import linalg
  
  
  def sum_numbers(x, y):
      """
      TODO: IMPLEMENT ME
      Sum two numbers.
  
      Args:
          x (int, float): first number in sum
          y (int, float): second number in sum
      Returns:
          Sum of x and y.
      """
      # replace the following line with an actual implementation that returns something
      #raise NotImplementedError
      return x+y
  def multiply_numbers(x, y):
      """
      TODO: IMPLEMENT ME
      Multiply two numbers.
  
      Args:
          x (int, float): first number in product
          y (int, float): second number in product
      Returns:
          Product of x and y.
      """
      
      # replace the following line with an actual implementation that returns something
      return x*y
  
  def create_add_matrix(x):
      """
      TODO: IMPLEMENT ME
      Step 1. Create a 3x3 numpy array whose elements are all ones.
      Step 2. Sum the array and the input array x.
      Step 3. Return the result
      
      Args:
          x (np.ndarray): a 2D numpy array
      Returns:
          output (np.ndarray): the operation result
      """
  
      # replace the following line with an actual implementation that returns something
      ones3=np.ones((3,3))
      sumwith=ones3+x
      return sumwith
      
  def indexing_aggregation(x, n):
      """
      TODO: IMPLEMENT ME
      Return the mean value of the first n elements of the input array x.
  
      Args:
          x (np.ndarray): a 1D numpy array
      Returns:
          output (float): the operation result
  
      """
      # replace the following line with an actual implementation that returns something
      return np.mean(np.array(x)[:n])
    
  def matrix_inverse(A):
      """
      TODO: IMPLEMENT ME
      Return the inverse of Matrix A.
      
      Checks for dimension mismatch
  
      Args:
          x (np.ndarray): a 2D numpy array
      Returns:
          output (np.ndarray): the operation result
  
      """
      # replace the following line with an actual implementation that returns something
      if (A.shape[0] != A.shape[1]):
          return None
      else:
          return np.linalg.inv(A) 
  
  